Integration, challenges, and future of artificial intelligence in critical care medicine: comprehensive applications from predictive models to clinical integration.

Qijian Ji, Yingwei Wu, Mingkun Yang, Jitao Liu, Xiaofeng Zhang, Yijia Lin, Weihang Hu

Abstract readReview

In one paragraph

Review in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Qijian Ji *Department of Critical Care Medicine, Xuyi Clinical College, Yangzhou University, Huai'an, Jiangsu, China.

Yingwei Wu *Department of Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.

Mingkun Yang *Department of Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.

Jitao LiuDepartment of Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.

Xiaofeng ZhangDepartment of Critical Care Medicine, The First People's Hospital of Aksu Region, Aksu, Xinjiang, China.

Yijia LinAffiliated Zhejiang Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.

Weihang HuDepartment of Critical Care Medicine, Zhejiang Hospital, Hangzhou, Zhejiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) in intensive care units (ICUs) has advanced rapidly since 2018, with core applications in sepsis prediction, mechanical ventilation management, and acute kidney injury (AKI) early warning, utilizing machine learning and deep learning models on multimodal data such as vital signs and electronic health records to achieve high predictive accuracy, including AUROC values up to 0.96 for sepsis. Despite these developments, widespread clinical adoption faces significant challenges, including limited prospective multicenter validation, the "black-box" nature of algorithms, integration into clinical workflows, and ethical concerns regarding fairness and transparency, necessitating rigorous evaluation and multidisciplinary collaboration to translate AI into routine critical care practice.

Indexed as

artificial intelligenceclinical integrationethical challengesintensive care medicinemultimodal datapredictive models

Identifiers

PMID42564754

DOI10.3389/fmed.2026.1874474

PMCPMC13445598

What Socratic holds

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LicenceCC BY

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.